Objective The study aimed to elucidate the population composition and seasonal distribution characteristics of mosquitoes in Laoshan District, Qingdao City, providing a scientific basis for the prevention and control of mosquito-borne infectious diseases. Methods Mosquito surveillance was conducted in Laoshan District in Qingdao City using light traps from April to November 2020-2022. Dominant mosquito species were identified using the Berger-Parker dominance index(I)and mosquito density was compared using the Kruskal-Wallis H test. The seasonal distribution characteristics of adult mosquitoes were analyzed using concentration and circular distribution method. Spearman rank correlation analysis was used to study the relationship between mosquito populations and meteorological factors with mean monthly temperature, mean monthly relative humidity, and mean monthly rainfall. Results The mean mosquito density for the trapping period was 9.771 females/(trap·night)in 2020,9.771 in 2021, and 9.427 in 2022. There was no statistically significant difference in mosquito density between different years(H = 0.095, P = 0.954). In total, 2 781 adult female mosquitoes comprising five species from four genera were captured over three years. Culex pipiens(I = 0.654), Aedes albopictus(I = 0.202), and Aedes aegypti(I = 0.118)were identified as dominant species. Seasonal fluctuations in the adult mosquitoes were unimodal, and primarily concentrated from June to September. The peak period of adult mosquito varied between years with statistical significance. (F = 3.838, P ＜ 0.05). Mosquito density was highly correlated with mean monthly temperature, mean monthly relative humidity and mean monthly precipitation(P＜ 0.05). Conclusions Cx. pipiens pallens, Ae. albopictus, and Ar. inamoratus are the dominant mosquito species in the Laoshan District of Qingdao City. The activity of adult mosquitoes is seasonal, and primarily concentrated in summer and autumn, during which the risk of mosquito-borne diseases such as dengue fever is increased.

Objective To investigate the effects of Echinococcus multilocularis infection on levels of memory T (Tm) cells and their subsets in lymph nodes of mice at different stages of infection, so as to provide new insights into immunotherapy for alveolarechinococcosis. MethodsTwenty-four C57BL/6J mice aged 6 to 9 weeks were randomly divided into the infection group and the control group, of 12 mice in each group. Mice in the infection group were administered with 3 000 E. multilocularis protoscoleces via portal venous injection, while animals in the control group were administered with an equal volume of physiological saline. Three mice from each group were sacrificed 4, 12 weeks and 24 weeks post-infection, and lymph nodes were sampled and stained with hematoxylin and eosin (HE) to investigate the histopathological changes of mouse lymph nodes in the infection group. The expression and localization of T lymphocyte surface markers CD3, CD4, and CD8 were observed in mouse lymph nodes using immunohistochemical staining. In addition, lymphocyte suspensions were prepared from mouse lymph nodes in both groups at different time points post-infection, and the levels of Tm cell subsets and their secreted cytokines were detected using flow cytometry. Results HE staining showed diffuse structural alterations in the subcapsular cortical and paracortical regions of mouse lymph nodes in the infection group 4 weeks post-infection with E. multilocularis. Immunohistochemical staining detected CD3, CD4 and CD8 expression in mouse lymph nodes in both groups. Flow cytometry revealed higher proportions of CD4⁺ Tm cells [(55.3 ± 4.8)% vs. (38.8 ± 6.1)%; t = -4.259, P < 0.05] and CD4⁺ tissue-resident Tm (Trm) cells [(57.7 ± 3.7)% vs. (34.1 ± 11.2)%; t = -3.990, P < 0.05] in mouse lymph nodes in the infection group than in the control group 4 weeks post-infection, and higher proportions of CD4⁺ Tm cells [(34.6 ± 3.2)% vs. (23.3 ± 7.5)%; t = -2.764, P < 0.05] and CD4⁺ Trm cells [(44.0 ± 1.9)% vs. (31.2 ± 1.5)%; t = -4.039, P < 0.05] in mouse lymph nodes in the infection group than in the control group 24 weeks post-infection. The proportions of CD8⁺ Tm cells were higher in the infection group than in the control group 4 weeks [(56.8 ± 2.7)% vs. (43.9 ± 5.2)%; t = -4.416, P < 0.01] and 12 weeks post-infection [(25.4 ± 2.7)% vs. (12.0 ± 2.6)%; t = -2.552, P < 0.05], while the proportions of tumor necrosis factor (TNF)-α⁺ CD4⁺ T cells [(15.7 ± 5.0)% vs. (49.4 ± 6.4)%; t = 7.150, P < 0.01], TNF-α⁺CD8⁺ T cells [(20.7 ± 5.5)% vs. (57.5 ± 8.4)%; t = -6.694, P < 0.01], and TNF-α⁺ CD8⁺ Tm cells [7.0% (1.0%) vs. 31.0% (11.0%); Z = -2.236, P < 0.05] were lower in the infection group than in the control group 24 weeks post-infection. Conclusions Tm cells levels are consistently increased in lymph nodes of mice at different stages of E. multilocularis infection, with Trm cells as the predominantly elevated subset. The impaired capacity of CD8⁺ Tm cells to secrete the effector molecule TNF-α in mouse lymph nodes at the late-stage infection may facilitate chronic parasitism of E. multilocularis.

Sigma-1 receptor (σ ₁R) has become a focus point of drug discovery for central nervous system (CNS) diseases. A series of novel 1-phenylethan-1-one O-(2-aminoethyl) oxime derivatives were synthesized. In vitro biological evaluation led to the identification of 1a, 14a, 15d and 16d as the most high-affinity (K _i < 4 nmol/L) and selective σ ₁R agonists. Among these, 15d, the most metabolically stable derivative exhibited high selectivity for σ ₁R in relation to σ ₂R and 52 other human targets. In addition to low CYP450 inhibition and induction, 15d also exhibited high brain permeability and excellent oral bioavailability. Importantly, 15d demonstrated effective antipsychotic potency, particularly for alleviating negative symptoms and improving cognitive impairment in experimental animal models, both of which are major challenges for schizophrenia treatment. Moreover, 15d produced no significant extrapyramidal symptoms, exhibiting superior pharmacological profiles in relation to current antipsychotic drugs. Mechanistically, 15d inhibited GSK3β and enhanced prefrontal BDNF expression and excitatory synaptic transmission in pyramidal neurons. Collectively, these in vivo proof-of-concept findings provide substantial experimental evidence to demonstrate that modulating σ ₁R represents a potential new therapeutic approach for schizophrenia. The novel chemical entity along with its favorable drug-like and pharmacological profile of 15d renders it a promising candidate for treating schizophrenia.

Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.
Humans ; Schizophrenia/pathology* ; Diffusion Tensor Imaging/methods* ; Male ; Female ; Adult ; Brain/metabolism* ; Young Adult ; Middle Aged ; White Matter/pathology* ; Gene Expression ; Nerve Net/diagnostic imaging* ; Graph Neural Networks

Personalized drug response prediction from molecular data is an important challenge in precision medicine for treating cancer. Computational methods have been widely explored and have become increasingly accurate in recent years. However, the clinical application of prediction methods is still in its infancy due to large discrepancies between preclinial models and patients. We present a novel disentangled synthesis transfer network (DiSyn) for drug response prediction specifically designed for transfer learning from preclinical models to clinical patients. DiSyn uses a domain separation network (DSN) to disentangle drug response related features, employs data synthesis technology to increase the sample size and iteratively trains for better feature disentanglement. DiSyn is pretrained on large-scale unlabeled cancer samples and validated by three datasets, The Cancer Genome Atlas (TCGA), Investigation of Serial Studies to Predict Your Therapeutic Response With Imaging And moLecular Analysis 2 (I-SPY2) and Novartis Institutes for Biomedical Research Patient-Derived Xenograft Encyclopedia (NIBR PDXE), achieving competitive performance with the state-of-the-art methods on cancer patients and mice. Furthermore, the application of DiSyn to thousands of breast cancer patients show the heterogeneity in drug responses and demonstrate its potential value in biomarker discovery and drug combination prediction.

Hepatocellular carcinoma (HCC) expresses abundant glycolytic enzymes and displays comprehensive glucose metabolism reprogramming. Aldolase A (ALDOA) plays a prominent role in glycolysis; however, little is known about its role in HCC development. In the present study, we aim to explore how ALDOA is involved in HCC proliferation. HCC proliferation was markedly suppressed both in vitro and in vivo following ALDOA knockout, which is consistent with ALDOA overexpression encouraging HCC proliferation. Mechanistically, ALDOA knockout partially limits the glycolytic flux in HCC cells. Meanwhile, ALDOA translocated to nuclei and directly interacted with c-Jun to facilitate its Thr93 phosphorylation by P21-activated protein kinase; ALDOA knockout markedly diminished c-Jun Thr93 phosphorylation and then dampened c-Jun transcription function. A crucial site Y364 mutation in ALDOA disrupted its interaction with c-Jun, and Y364S ALDOA expression failed to rescue cell proliferation in ALDOA deletion cells. In HCC patients, the expression level of ALDOA was correlated with the phosphorylation level of c-Jun (Thr93) and poor prognosis. Remarkably, hepatic ALDOA was significantly upregulated in the promotion and progression stages of diethylnitrosamine-induced HCC models, and the knockdown of A ldoa strikingly decreased HCC development in vivo. Our study demonstrated that ALDOA is a vital driver for HCC development by activating c-Jun-mediated oncogene transcription, opening additional avenues for anti-cancer therapies.

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Hepatocellular carcinoma(HCC)expresses abundant glycolytic enzymes and displays comprehensive glucose metabolism reprogramming.Aldolase A(ALDOA)plays a prominent role in glycolysis;however,little is known about its role in HCC development.In the present study,we aim to explore how ALDOA is involved in HCC proliferation.HCC proliferation was markedly suppressed both in vitro and in vivo following ALDOA knockout,which is consistent with ALDOA overexpression encouraging HCC prolifera-tion.Mechanistically,ALDOA knockout partially limits the glycolytic flux in HCC cells.Meanwhile,ALDOA translocated to nuclei and directly interacted with c-Jun to facilitate its Thr93 phosphorylation by P21-activated protein kinase;ALDOA knockout markedly diminished c-Jun Thr93 phosphorylation and then dampened c-Jun transcription function.A crucial site Y364 mutation in ALDOA disrupted its interaction with c-Jun,and Y364S ALDOA expression failed to rescue cell proliferation in ALDOA deletion cells.In HCC patients,the expression level of ALDOA was correlated with the phosphorylation level of c-Jun(Thr93)and poor prognosis.Remarkably,hepatic ALDOA was significantly upregulated in the promotion and progression stages of diethylnitrosamine-induced HCC models,and the knockdown of Aldoa strikingly decreased HCC development in vivo.Our study demonstrated that ALDOA is a vital driver for HCC development by activating c-Jun-mediated oncogene transcription,opening additional avenues for anti-cancer therapies.

Personalized drug response prediction from molecular data is an important challenge in precision medicine for treating cancer.Computational methods have been widely explored and have become increasingly accurate in recent years.However,the clinical application of prediction methods is still in its infancy due to large discrepancies between preclinial models and patients.We present a novel disentangled synthesis transfer network(DiSyn)for drug response prediction specifically designed for transfer learning from preclinical models to clinical patients.DiSyn uses a domain separation network(DSN)to disentangle drug response related features,employs data synthesis technology to increase the sample size and iteratively trains for better feature disentanglement.DiSyn is pretrained on large-scale unlabeled cancer samples and validated by three datasets,The Cancer Genome Atlas(TCGA),Investigation of Serial Studies to Predict Your Therapeutic Response With Imaging And moLecular Analysis 2(I-SPY2)and Novartis Institutes for Biomedical Research Patient-Derived Xenograft Encyclopedia(NIBR PDXE),achieving competitive performance with the state-of-the-art methods on cancer patients and mice.Furthermore,the application of DiSyn to thousands of breast cancer patients show the heterogeneity in drug responses and demonstrate its potential value in biomarker discovery and drug combination prediction.

Objective:To investigate the effect of elimination,combination,rearrangement and simplification(ECRS)management combined with risk assessment on reducing the incidence of multidrug-resistant bacteria infections of patients who received mechanical ventilation in intensive care unit(ICU).Methods:The management mode of prevention and control for multidrug-resistant bacteria infections of patients in ICU was optimized on the basis of ECRS management combined with risk assessment.A total of 600 patients who received mechanical ventilation in ICU of Jiuquan Hospital of Shanghai General Hospital(Jiuquan People's Hospital)from January 2022 to December 2023 were selected.According to different management methods,these patients were divided into a control group and an observation group,with 300 cases in each group.The control group was managed by using the risk assessment management method,while the observation group was managed by using the ECRS management on the basis of risk assessment management method.The indicators of respiratory function,patients'satisfaction score,stay time in ICU,time of mechanical ventilation and incidence of multidrug-resistant bacteria were compared between the two groups.Results:The mean value of the ratio of forced expiratory volume in one second(FEV1)to forced vital capacity(FVC)(FEV1/FVC),and the FEV1 level in observation group by using ECRS management combined with risk assessment method were respectively(78.69±4.65)%and(1.58±0.24)L,both of which were higher than those of control group,and the differences of them between two groups were statistically significant(t=16.483,11.742,P<0.05).The average scores of work efficiency,emergency response capability,professional ethics,isolation and resettlement,and overall patients'satisfaction in the observation group were respectively(23.12±1.20),(23.34±1.08),(23.65±1.10),(23.80±1.05)and(92.24±4.37),all of which were higher than those in the control group,and the differences of them between two groups were statistically significant(t=22.176,27.903,22.373,31.364,13.963,P<0.05).The average ICU stay time and the average time of mechanical ventilation were respectively(14.15±1.60)and(9.15±2.13)days in the observation group,both of which were lower than those in the control group,and the differences of them between two groups were statistically significant(t=16.872,15.410,P<0.05).The incidence of multidrug-resistant bacteria was 0.33%in 300 patients of the observation group,which was lower than that of the control group,with a statistically significant difference(x2=4.561,P<0.05).Conclusion:The application of ECRS management combined with risk assessment in the management of ICU for patients who receive mechanical ventilation can protect respiratory function of patients,and decrease the risk of occurring the infection of multidrug-resistant bacteria,and reduce ICU stay time and the time of mechanical ventilation of patients,and improve patients'satisfaction.